Abstract: In this paper, we present a comparative study of the
genetic algorithms and Hessian-s methods for optimal research of the
active powers in an electric network of power. The objective function
which is the performance index of production of electrical energy is
minimized by satisfying the constraints of the equality type and
inequality type initially by the Hessian-s methods and in the second
time by the genetic Algorithms. The results found by the application
of AG for the minimization of the electric production costs of power
are very encouraging. The algorithms seem to be an effective
technique to solve a great number of problems and which are in
constant evolution. Nevertheless it should be specified that the
traditional binary representation used for the genetic algorithms
creates problems of optimization of management of the large-sized
networks with high numerical precision.
Abstract: This paper presents a customized deformable model
for the segmentation of abdominal and thoracic aortic aneurysms in
CTA datasets. An important challenge in reliably detecting aortic
aneurysm is the need to overcome problems associated with intensity
inhomogeneities and image noise. Level sets are part of an important
class of methods that utilize partial differential equations (PDEs) and
have been extensively applied in image segmentation. A Gaussian
kernel function in the level set formulation, which extracts the local
intensity information, aids the suppression of noise in the extracted
regions of interest and then guides the motion of the evolving contour
for the detection of weak boundaries. The speed of curve evolution
has been significantly improved with a resulting decrease in
segmentation time compared with previous implementations of level
sets. The results indicate the method is more effective than other
approaches in coping with intensity inhomogeneities.
Abstract: This paper presents an evolutionary method for designing
electronic circuits and numerical methods associated with
monitoring systems. The instruments described here have been used
in studies of weather and climate changes due to global warming, and
also in medical patient supervision. Genetic Programming systems
have been used both for designing circuits and sensors, and also for
determining sensor parameters. The authors advance the thesis that
the software side of such a system should be written in computer
languages with a strong mathematical and logic background in order
to prevent software obsolescence, and achieve program correctness.
Abstract: The software evolution control requires a deep
understanding of the changes and their impact on different system
heterogeneous artifacts. And an understanding of descriptive
knowledge of the developed software artifacts is a prerequisite
condition for the success of the evolutionary process.
The implementation of an evolutionary process is to make changes
more or less important to many heterogeneous software artifacts such
as source code, analysis and design models, unit testing, XML
deployment descriptors, user guides, and others. These changes can
be a source of degradation in functional, qualitative or behavioral
terms of modified software. Hence the need for a unified approach
for extraction and representation of different heterogeneous artifacts
in order to ensure a unified and detailed description of heterogeneous
software artifacts, exploitable by several software tools and allowing
to responsible for the evolution of carry out the reasoning change
concerned.
Abstract: The problems with high complexity had been the challenge in combinatorial problems. Due to the none-determined and polynomial characteristics, these problems usually face to unreasonable searching budget. Hence combinatorial optimizations attracted numerous researchers to develop better algorithms. In recent academic researches, most focus on developing to enhance the conventional evolutional algorithms and facilitate the local heuristics, such as VNS, 2-opt and 3-opt. Despite the performances of the introduction of the local strategies are significant, however, these improvement cannot improve the performance for solving the different problems. Therefore, this research proposes a meta-heuristic evolutional algorithm which can be applied to solve several types of problems. The performance validates BBEA has the ability to solve the problems even without the design of local strategies.
Abstract: Electron back-scattered diffraction was used to follow the evolution of microstructure from the base metal to the stir zone (SZ) in a duplex stainless steel subjected to friction stir welding. In the stir zone (SZ), a continuous dynamic recrystallization (CDRX) was evidenced for ferrite, while it was suggested that a static recrystallization together with CDRX may occur for austenite. It was found that ferrite and austenite grains in the SZ take a typical shear texture of bcc and fcc materials respectively.
Abstract: Text categorization is the problem of classifying text
documents into a set of predefined classes. After a preprocessing
step, the documents are typically represented as large sparse vectors.
When training classifiers on large collections of documents, both the
time and memory restrictions can be quite prohibitive. This justifies
the application of feature selection methods to reduce the
dimensionality of the document-representation vector. In this paper,
we present three feature selection methods: Information Gain,
Support Vector Machine feature selection called (SVM_FS) and
Genetic Algorithm with SVM (called GA_SVM). We show that the
best results were obtained with GA_SVM method for a relatively
small dimension of the feature vector.
Abstract: One of the main objectives of order reduction is to
design a controller of lower order which can effectively control the
original high order system so that the overall system is of lower
order and easy to understand. In this paper, a simple method is
presented for controller design of a higher order discrete system.
First the original higher order discrete system in reduced to a lower
order model. Then a Proportional Integral Derivative (PID)
controller is designed for lower order model. An error minimization
technique is employed for both order reduction and controller
design. For the error minimization purpose, Differential Evolution
(DE) optimization algorithm has been employed. DE method is
based on the minimization of the Integral Squared Error (ISE)
between the desired response and actual response pertaining to a
unit step input. Finally the designed PID controller is connected to
the original higher order discrete system to get the desired
specification. The validity of the proposed method is illustrated
through a numerical example.
Abstract: The objective of this work was to examine the
changes in the microstructure and macro physical properties caused
by the carbonation of normalised CEM II mortar. Samples were
prepared and subjected to accelerated carbonation at 20°C, 65%
relative humidity and 20% CO2 concentration. On the microstructure
scale, the evolutions of the cumulative pore volume, pore size
distribution, and specific surface area during carbonation were
calculated from the adsorption desorption isotherms of nitrogen. We
also examined the evolution of macro physical properties such as the
porosity accessible to water, the gas permeability, and thermal
conductivity. The conflict between the results of nitrogen porosity
and water porosity indicated that the porous domains explored using
these two techniques are different and help to complementarily
evaluate the effects of carbonation. This is a multi-scale study where
results on microstructural changes can help to explain the evolution
of macro physical properties.
Abstract: This paper explores the idea of globalisation and
considers accounting-s role in that process in order to develop new
spaces for accounting research. That-s why in this paper we are
looking for questions not necessary for answers. Adopting an
'alternative' view of accounting it-s related to the fact that we sees
accounting as social and evolutionist process, that pays heed to those
voices arguing for greater social and environmental justice, and that
draws attention to the role of accounting researchers in the process of
globalisation. The paper defines globalisation and expands the
globalisation and accounting research agenda introducing in this
context the harmonization process in accounting. There are the two
main systems which are disputing the first stage of being the
benchmark: GAAP and IFRS. Each of them has his pluses and
minuses on being the selected one. Due to this fact a convergence of
the two, joining the advantages and disadvantages of the two should
be the solution for an unique international accounting solution. Is this
idea realizable, what steps has been made until now, what should be
done in the future. The paper is emphasising the role of the cultural
differences in the process of imposing of an unique international
accounting system by the global organizations..
Abstract: Mammalian genomes contain large number of
retroelements (SINEs, LINEs and LTRs) which could affect
expression of protein coding genes through associated transcription
factor binding sites (TFBS). Activity of the retroelement-associated
TFBS in many genes is confirmed experimentally but their global
functional impact remains unclear. Human SINEs (Alu repeats) and
mouse SINEs (B1 and B2 repeats) are known to be clustered in GCrich
gene rich genome segments consistent with the view that they
can contribute to regulation of gene expression. We have shown
earlier that Alu are involved in formation of cis-regulatory modules
(clusters of TFBS) in human promoters, and other authors reported
that Alu located near promoter CpG islands have an increased
frequency of CpG dinucleotides suggesting that these Alu are
undermethylated. Human Alu and mouse B1/B2 elements have an
internal bipartite promoter for RNA polymerase III containing
conserved sequence motif called B-box which can bind basal
transcription complex TFIIIC. It has been recently shown that TFIIIC
binding to B-box leads to formation of a boundary which limits
spread of repressive chromatin modifications in S. pombe. SINEassociated
B-boxes may have similar function but conservation of
TFIIIC binding sites in SINEs located near mammalian promoters
has not been studied earlier. Here we analysed abundance and
distribution of retroelements (SINEs, LINEs and LTRs) in annotated
sequences of the Database of mammalian transcription start sites
(DBTSS). Fractions of SINEs in human and mouse promoters are
slightly lower than in all genome but >40% of human and mouse
promoters contain Alu or B1/B2 elements within -1000 to +200 bp
interval relative to transcription start site (TSS). Most of these SINEs
is associated with distal segments of promoters (-1000 to -200 bp
relative to TSS) indicating that their insertion at distances >200 bp
upstream of TSS is tolerated during evolution. Distribution of SINEs
in promoters correlates negatively with the distribution of CpG
sequences. Using analysis of abundance of 12-mer motifs from the
B1 and Alu consensus sequences in genome and DBTSS it has been
confirmed that some subsegments of Alu and B1 elements are poorly
conserved which depends in part on the presence of CpG
dinucleotides. One of these CpG-containing subsegments in B1
elements overlaps with SINE-associated B-box and it shows better
conservation in DBTSS compared to genomic sequences. It has been
also studied conservation in DBTSS and genome of the B-box
containing segments of old (AluJ, AluS) and young (AluY) Alu
repeats and found that CpG sequence of the B-box of old Alu is
better conserved in DBTSS than in genome. This indicates that Bbox-
associated CpGs in promoters are better protected from
methylation and mutation than B-box-associated CpGs in genomic
SINEs. These results are consistent with the view that potential
TFIIIC binding motifs in SINEs associated with human and mouse
promoters may be functionally important. These motifs may protect
promoters from repressive histone modifications which spread from
adjacent sequences. This can potentially explain well known
clustering of SINEs in GC-rich gene rich genome compartments and
existence of unmethylated CpG islands.
Abstract: Integrins are a large family of multidomain α/β cell
signaling receptors. Some integrins contain an additional inserted I
domain, whose earliest expression appears to be with the chordates,
since they are observed in the urochordates Ciona intestinalis (vase
tunicate) and Halocynthia roretzi (sea pineapple), but not in integrins
of earlier diverging species. The domain-s presence is viewed as a
hallmark of integrins of higher metazoans, however in vertebrates,
there are clearly three structurally-different classes: integrins without
I domains, and two groups of integrins with I domains but separable
by the presence or absence of an additional αC helix. For example,
the αI domains in collagen-binding integrins from Osteichthyes
(bony fish) and all higher vertebrates contain the specific αC helix,
whereas the αI domains in non-collagen binding integrins from
vertebrates and the αI domains from earlier diverging urochordate
integrins, i.e. tunicates, do not. Unfortunately, within the early
chordates, there is an evolutionary gap due to extinctions between the
tunicates and cartilaginous fish. This, coupled with a knowledge gap
due to the lack of complete genomic data from surviving species,
means that the origin of collagen-binding αC-containing αI domains
remains unknown. Here, we analyzed two available genomes from
Callorhinchus milii (ghost shark/elephant shark; Chondrichthyes –
cartilaginous fish) and Petromyzon marinus (sea lamprey;
Agnathostomata), and several available Expression Sequence Tags
from two Chondrichthyes species: Raja erinacea (little skate) and
Squalus acanthias (dogfish shark); and Eptatretus burgeri (inshore
hagfish; Agnathostomata), which evolutionary reside between the
urochordates and osteichthyes. In P. marinus, we observed several
fragments coding for the αC-containing αI domain, allowing us to
shed more light on the evolution of the collagen-binding integrins.
Abstract: The aim of this work is to present a multi-objective optimization method to find maximum efficiency kinematics for a flapping wing unmanned aerial vehicle. We restrained our study to rectangular wings with the same profile along the span and to harmonic dihedral motion. It is assumed that the birdlike aerial vehicle (whose span and surface area were fixed respectively to 1m and 0.15m2) is in horizontal mechanically balanced motion at fixed speed. We used two flight physics models to describe the vehicle aerodynamic performances, namely DeLaurier-s model, which has been used in many studies dealing with flapping wings, and the model proposed by Dae-Kwan et al. Then, a constrained multi-objective optimization of the propulsive efficiency is performed using a recent evolutionary multi-objective algorithm called є-MOEA. Firstly, we show that feasible solutions (i.e. solutions that fulfil the imposed constraints) can be obtained using Dae-Kwan et al.-s model. Secondly, we highlight that a single objective optimization approach (weighted sum method for example) can also give optimal solutions as good as the multi-objective one which nevertheless offers the advantage of directly generating the set of the best trade-offs. Finally, we show that the DeLaurier-s model does not yield feasible solutions.
Abstract: This paper analysis the tourism development on the
Red Sea in Egypt (west bank) and the needed ongoing action toward
a sustainable approach. It addresses, at the first, the development's
evolution occurred in the coastal area, the environmental effects it
left, and how to minimize those impacts in the future. The second
main point is dealing with the most important issues that hinder the
achievement of sustainable tourism development on the Red Sea
coast and how we can overcome them in the future.
Abstract: German electricity European options on futures using
Lévy processes for the underlying asset are examined. Implied
volatility evolution, under each of the considered models, is
discussed after calibrating for the Merton jump diffusion (MJD),
variance gamma (VG), normal inverse Gaussian (NIG), Carr, Geman,
Madan and Yor (CGMY) and the Black and Scholes (B&S) model.
Implied volatility is examined for the entire sample period, revealing
some curious features about market evolution, where data fitting
performances of the five models are compared. It is shown that
variance gamma processes provide relatively better results and that
implied volatility shows significant differences through time, having
increasingly evolved. Volatility changes for changed uncertainty, or
else, increasing futures prices and there is evidence for the need to
account for seasonality when modelling both electricity spot/futures
prices and volatility.
Abstract: Ever since industrial revolution began, our ecosystem
has changed. And indeed, the negatives outweigh the positives.
Industrial waste usually released into all kinds of body of water, such
as river or sea. Tempeh waste is one example of waste that carries
many hazardous and unwanted substances that will affect the
surrounding environment. Tempeh is a popular fermented food in
Asia which is rich in nutrients and active substances. Tempeh liquid
waste- in particular- can cause an air pollution, and if penetrates
through the soil, it will contaminates ground-water, making it
unavailable for the water to be consumed. Moreover, bacteria will
thrive within the polluted water, which often responsible for causing
many kinds of diseases. The treatment used for this chemical waste is
biological treatment such as constructed wetland and activated
sludge. These kinds of treatment are able to reduce both physical and
chemical parameters altogether such as temperature, TSS, pH, BOD,
COD, NH3-N, NO3-N, and PO4-P. These treatments are implemented
before the waste is released into the water. The result is a
comparation between constructed wetland and activated sludge,
along with determining which method is better suited to reduce the
physical and chemical subtances of the waste.
Abstract: In any trust model, the two information sources that a peer relies on to predict trustworthiness of another peer are direct experience as well as reputation. These two vital components evolve over time. Trust evolution is an important issue, where the objective is to observe a sequence of past values of a trust parameter and determine the future estimates. Unfortunately, trust evolution algorithms received little attention and the proposed algorithms in the literature do not comply with the conditions and the nature of trust. This paper contributes to this important problem in the following ways: (a) presents an algorithm that manages and models trust evolution in a P2P environment, (b) devises new mechanisms for effectively maintaining trust values based on the conditions that influence trust evolution , and (c) introduces a new methodology for incorporating trust-nurture incentives into the trust evolution algorithm. Simulation experiments are carried out to evaluate our trust evolution algorithm.
Abstract: A key element of many distribution systems is the
routing and scheduling of vehicles servicing a set of customers. A
wide variety of exact and approximate algorithms have been
proposed for solving the vehicle routing problems (VRP). Exact
algorithms can only solve relatively small problems of VRP, which is
classified as NP-Hard. Several approximate algorithms have proven
successful in finding a feasible solution not necessarily optimum.
Although different parts of the problem are stochastic in nature; yet,
limited work relevant to the application of discrete event system
simulation has addressed the problem. Presented here is optimization
using simulation of VRP; where, a simplified problem has been
developed in the ExtendSimTM simulation environment; where,
ExtendSimTM evolutionary optimizer is used to minimize the total
transportation cost of the problem. Results obtained from the model
are very satisfactory. Further complexities of the problem are
proposed for consideration in the future.
Abstract: Deoxyribonucleic Acid or DNA computing has
emerged as an interdisciplinary field that draws together chemistry,
molecular biology, computer science and mathematics. Thus, in this
paper, the possibility of DNA-based computing to solve an absolute
1-center problem by molecular manipulations is presented. This is
truly the first attempt to solve such a problem by DNA-based
computing approach. Since, part of the procedures involve with
shortest path computation, research works on DNA computing for
shortest path Traveling Salesman Problem, in short, TSP are reviewed.
These approaches are studied and only the appropriate one is adapted
in designing the computation procedures. This DNA-based
computation is designed in such a way that every path is encoded by
oligonucleotides and the path-s length is directly proportional to the
length of oligonucleotides. Using these properties, gel electrophoresis
is performed in order to separate the respective DNA molecules
according to their length. One expectation arise from this paper is that
it is possible to verify the instance absolute 1-center problem using
DNA computing by laboratory experiments.
Abstract: This paper presents a new Hybrid Fuzzy (HF) PID type controller based on Genetic Algorithms (GA-s) for solution of the Automatic generation Control (AGC) problem in a deregulated electricity environment. In order for a fuzzy rule based control system to perform well, the fuzzy sets must be carefully designed. A major problem plaguing the effective use of this method is the difficulty of accurately constructing the membership functions, because it is a computationally expensive combinatorial optimization problem. On the other hand, GAs is a technique that emulates biological evolutionary theories to solve complex optimization problems by using directed random searches to derive a set of optimal solutions. For this reason, the membership functions are tuned automatically using a modified GA-s based on the hill climbing method. The motivation for using the modified GA-s is to reduce fuzzy system effort and take large parametric uncertainties into account. The global optimum value is guaranteed using the proposed method and the speed of the algorithm-s convergence is extremely improved, too. This newly developed control strategy combines the advantage of GA-s and fuzzy system control techniques and leads to a flexible controller with simple stricture that is easy to implement. The proposed GA based HF (GAHF) controller is tested on a threearea deregulated power system under different operating conditions and contract variations. The results of the proposed GAHF controller are compared with those of Multi Stage Fuzzy (MSF) controller, robust mixed H2/H∞ and classical PID controllers through some performance indices to illustrate its robust performance for a wide range of system parameters and load changes.